DeepSeek vs Gemini: We Tested Both on 5 Real Tasks (2026)

DeepSeek vs Gemini: The Full Breakdown

This is a fascinating David-vs-Goliath matchup. DeepSeek, the open-source model from China that stunned the AI industry with its performance-per-dollar ratio, goes head-to-head against Gemini, Google’s flagship AI backed by arguably the world’s most powerful tech infrastructure.

We ran both through 5 identical real-world tasks and scored them head-to-head. No marketing fluff — just actual outputs compared side by side.

Models tested: DeepSeek-V3 (free chat interface) and Gemini 2.0 Flash (free tier via gemini.google.com)

Test 1: Research Summary

Prompt: “Summarize the key differences between RISC-V and ARM processor architectures, including pros, cons, and which industries are adopting each.”

DeepSeek’s Output

RISC-V vs ARM: Key Differences

Architecture & Licensing: RISC-V is an open-source instruction set architecture (ISA) — anyone can design, manufacture, and sell RISC-V chips without paying royalties. ARM, by contrast, is proprietary: companies like Apple, Qualcomm, and Samsung pay significant licensing fees to ARM Holdings (now owned by SoftBank) to use the architecture.

Design Philosophy: Both are RISC (Reduced Instruction Set Computer) architectures, but RISC-V was designed from scratch in 2010 at UC Berkeley with the benefit of hindsight. It has a cleaner, more modular design with a small base instruction set and optional extensions. ARM has evolved over 30+ years, accumulating some legacy complexity.

Ecosystem Maturity: ARM’s ecosystem is decades ahead — mature toolchains, vast software libraries, proven silicon designs. RISC-V is catching up rapidly but still lacks the breadth of ARM’s software and hardware support.

Industry Adoption:

• ARM: Dominates mobile (99%+ of smartphones), expanding into servers (AWS Graviton, Ampere), laptops (Apple M-series), and automotive.
• RISC-V: Growing in IoT, embedded systems, and AI accelerators. China is investing heavily (Alibaba’s Xuantie series). Western Digital and SiFive are major advocates. India has adopted RISC-V as a national priority.

Pros/Cons:

• RISC-V Pros: No licensing fees, full customization, geopolitical independence, modular design
• RISC-V Cons: Immature ecosystem, fewer off-the-shelf designs, smaller talent pool
• ARM Pros: Massive ecosystem, proven performance, power efficiency, broad industry support
• ARM Cons: Licensing costs, less design freedom, single-vendor dependency

Score: 9/10 — Excellent structure, accurate detail, covers both technical and business angles. The China/geopolitical angle shows real depth.

Gemini’s Output

RISC-V vs. ARM: A Comparison

RISC-V and ARM are both reduced instruction set computer (RISC) architectures, but they differ significantly in their approach to licensing, ecosystem maturity, and target markets.

Licensing: The most fundamental difference is that RISC-V is open-source and royalty-free. Anyone can implement a RISC-V processor without paying licensing fees. ARM, on the other hand, operates on a licensing model where companies pay ARM Holdings for the right to use their instruction set architecture and, optionally, their core designs.

Ecosystem: ARM has a massive, mature ecosystem built over decades. This includes extensive software support, development tools, and a wide range of pre-designed processor cores. RISC-V’s ecosystem is younger and smaller, though it’s growing rapidly with increasing community and corporate support.

Customization: RISC-V’s modular design allows for extensive customization. Designers can add custom extensions to the base ISA to optimize for specific workloads. While ARM offers some customization options (like ARM’s Custom Instructions program), the degree of flexibility is generally less than what RISC-V provides.

Adoption:

• ARM: Smartphones (nearly universal), tablets, embedded systems, and increasingly in laptops (Apple Silicon) and data centers (AWS Graviton).
• RISC-V: Embedded systems, IoT devices, AI/ML accelerators, and growing interest in other sectors. Companies like SiFive, Andes Technology, and major players in China are driving adoption.

Pros and Cons:

• RISC-V: ✅ Free, customizable, no vendor lock-in. ❌ Smaller ecosystem, fewer proven high-performance designs.
• ARM: ✅ Mature ecosystem, proven performance, wide software support. ❌ Licensing costs, less flexibility.

Score: 8/10 — Solid, accurate, well-organized. Slightly less detailed than DeepSeek’s response — misses the geopolitical dimension and specific company examples are thinner.

Test 1 Winner: DeepSeek 🏆

Test 2: Python Coding Challenge

Prompt: “Write a Python function that takes a list of stock prices (daily closing prices) and returns the maximum profit possible from a single buy-sell transaction. Include edge cases and time complexity analysis.”

DeepSeek’s Output

def max_profit(prices: list[float]) -> float:
    """
    Calculate maximum profit from a single buy-sell transaction.
    
    Uses Kadane's algorithm variant - track minimum price seen so far
    and maximum profit achievable at each step.
    
    Time Complexity: O(n) - single pass
    Space Complexity: O(1) - constant extra space
    
    Edge cases:
    - Empty list or single element: return 0 (no transaction possible)
    - Monotonically decreasing prices: return 0 (no profitable transaction)
    - All same prices: return 0
    - Negative prices: handled correctly (though unusual in practice)
    
    Args:
        prices: List of daily closing prices
    Returns:
        Maximum profit (>= 0). Returns 0 if no profitable transaction exists.
    """
    if len(prices) < 2:
        return 0
    
    min_price = prices[0]
    max_profit_val = 0
    
    for price in prices[1:]:
        # Could we profit by selling at today's price?
        profit = price - min_price
        max_profit_val = max(max_profit_val, profit)
        # Update minimum price seen so far
        min_price = min(min_price, price)
    
    return max_profit_val


# Test cases
assert max_profit([7, 1, 5, 3, 6, 4]) == 5  # Buy at 1, sell at 6
assert max_profit([7, 6, 4, 3, 1]) == 0      # Decreasing - no profit
assert max_profit([]) == 0                     # Empty
assert max_profit([5]) == 0                    # Single element
assert max_profit([2, 2, 2]) == 0             # All same
assert max_profit([1, 2]) == 1                # Minimum case
print("All tests passed!")

Score: 10/10 — Textbook-perfect. Clean implementation, comprehensive edge cases, proper type hints, excellent docstring with complexity analysis, and test cases included.

Gemini’s Output

def max_profit(prices):
    """
    Finds the maximum profit from a single buy and sell transaction.

    Args:
        prices: A list of daily closing stock prices.

    Returns:
        The maximum profit possible. Returns 0 if no profit is possible.
    """
    if not prices or len(prices) < 2:
        return 0

    min_price = float('inf')
    max_profit = 0

    for price in prices:
        if price < min_price:
            min_price = price
        elif price - min_price > max_profit:
            max_profit = price - min_price

    return max_profit

# Time Complexity: O(n) where n is the length of the prices list.
# We iterate through the list once.
# Space Complexity: O(1) - we only use a constant amount of extra space.

# Examples:
print(max_profit([7, 1, 5, 3, 6, 4]))  # Output: 5 (Buy at 1, sell at 6)
print(max_profit([7, 6, 4, 3, 1]))      # Output: 0 (No profitable transaction)
print(max_profit([]))                     # Output: 0

Score: 8/10 — Correct algorithm, works fine. But: no type hints, complexity analysis is a comment rather than in the docstring, fewer test cases, uses float('inf') instead of the cleaner prices[0] initialization, shadows the function name with max_profit variable.

Test 2 Winner: DeepSeek 🏆

Test 3: Creative Writing

Prompt: “Write a 150-word flash fiction piece about an astronaut who discovers that the stars they’re navigating by have been rearranged.”

DeepSeek’s Output

Commander Lin checked the star charts for the third time. Orion’s belt — two stars instead of three. Polaris — shifted twelve degrees east.

“Navigation, confirm our position.”

“That’s the problem, Commander. I can confirm our position. It’s the universe that’s wrong.”

She floated to the observation deck. The constellations she’d memorized as a girl in Chengdu were scrambled — familiar shapes warped into alien alphabets. Cassiopeia’s W had become something like a question mark.

The radio crackled. Every station, same message: “Do not navigate by starlight. Repeat: do not navigate by starlight.”

Lin pressed her forehead against the cold glass. Whoever — whatever — had moved the stars had done it with precision. Not chaos. A pattern she almost recognized.

She pulled out a blank chart and began drawing new constellations. If someone had rewritten the sky, she intended to learn to read it.

Score: 9/10 — Atmospheric, compelling, great pacing. “It’s the universe that’s wrong” is an excellent line. The ending shows agency and resilience. Strong character voice.

Gemini’s Output

The star map was wrong. Not slightly off, not a calibration error — fundamentally, impossibly wrong.

Captain Elara Voss stared at the display, then out the viewport. Betelgeuse, the red giant that should have anchored Orion’s shoulder, now burned cold and blue thirty degrees from where it belonged. The Pleiades cluster had scattered like seeds across an unfamiliar patch of darkness.

“ARIA, run diagnostics on the navigation array.”

“Navigation array functioning within normal parameters, Captain. The discrepancy is external.”

External. The word hung in the recycled air. Someone — something — had rearranged the stars themselves. Not randomly. Elara could see it now, the deliberate geometry, the impossible angles forming shapes that tugged at something primal in her mind.

Letters. They were letters.

She whispered the message aloud, and the ship’s lights flickered in response.

Score: 9/10 — Excellent atmosphere and tension. “Scattered like seeds” is evocative. The reveal that the stars form letters is a great twist. Slightly more conventional sci-fi style but very effective.

Test 3 Winner: Tie 🤝

Test 4: Mathematical Reasoning

Prompt: “A factory produces widgets in batches. Each batch has a 3% defect rate. A quality inspector randomly selects 10 widgets from a batch of 500. What’s the probability that exactly 2 of the 10 selected widgets are defective? Show your work step by step.”

DeepSeek’s Output

Setting up the problem:

This is a hypergeometric distribution problem (sampling without replacement from a finite population).

Parameters:

• Population size (N) = 500
• Number of defective widgets (K) = 500 × 0.03 = 15
• Sample size (n) = 10
• We want exactly k = 2 defectives

Hypergeometric formula:

P(X = k) = C(K, k) × C(N-K, n-k) / C(N, n)

P(X = 2) = C(15, 2) × C(485, 8) / C(500, 10)

Calculating each component:

C(15, 2) = 15! / (2! × 13!) = (15 × 14) / 2 = 105

For the large combinations, let me compute the ratio:

C(485, 8) / C(500, 10) = [485! / (8! × 477!)] / [500! / (10! × 490!)]

Simplifying: = [10! × 490! × 485!] / [8! × 477! × 500!]

= [10 × 9 × 490 × 489 × 488 × 487 × 486 × 485 × 484 × 483 × 482 × 481 × 480 × 479 × 478] / [500 × 499 × 498 × 497 × 496 × 495 × 494 × 493 × 492 × 491]

…this gets unwieldy. Let me use the practical approach:

P(X = 2) = C(15,2) × C(485,8) / C(500,10)

Computing numerically: ≈ 0.0746 or about 7.46%

Verification with binomial approximation: Since n/N = 10/500 = 0.02 (small sampling fraction), we can approximate with binomial: P(X = 2) ≈ C(10,2) × 0.03² × 0.97⁸ = 45 × 0.0009 × 0.7837 ≈ 0.0317 or 3.17%

Wait — the hypergeometric and binomial should be close here. Let me recompute…

Actually, the binomial approximation is the better check: P = C(10,2) × 0.03² × 0.97⁸ = 45 × 0.0009 × 0.7837 = 0.0317 (3.17%)

The hypergeometric result should be very close to this given the small sampling fraction. My earlier computation had an error. The correct answer is approximately 3.17%.

Score: 7/10 — Correctly identifies this as hypergeometric, shows good mathematical thinking, but makes a computational error in the hypergeometric calculation then catches it via the binomial check. Self-correction is good, but the journey was messy.

Gemini’s Output

Identifying the Distribution:

This is a hypergeometric distribution problem because we’re sampling without replacement from a finite population.

Given information:

• Total widgets (N) = 500
• Defective widgets (K) = 3% of 500 = 15
• Non-defective widgets = 485
• Sample size (n) = 10
• We want P(X = 2)

Hypergeometric Formula:

P(X = k) = [C(K,k) × C(N-K, n-k)] / C(N,n)

Step 1: Calculate C(15, 2) C(15, 2) = 15! / (2! × 13!) = (15 × 14) / (2 × 1) = 105

Step 2: Calculate C(485, 8) C(485, 8) = 485! / (8! × 477!) This is a very large number: ≈ 2.725 × 10¹⁸

Step 3: Calculate C(500, 10) C(500, 10) = 500! / (10! × 490!) This is also very large: ≈ 2.634 × 10²⁰

Step 4: Compute the probability P(X = 2) = (105 × 2.725 × 10¹⁸) / (2.634 × 10²⁰) P(X = 2) = 2.861 × 10²⁰ / 2.634 × 10²⁰ P(X = 2) ≈ 0.1086 or about 10.86%

Note: We can verify this approximately using the binomial distribution (since our sample is small relative to the population): P(X = 2) ≈ C(10,2) × (0.03)² × (0.97)⁸ = 45 × 0.0009 × 0.7837 ≈ 0.0317 or 3.17%

The hypergeometric gives a slightly different result because it accounts for the finite population, but both indicate that finding exactly 2 defective widgets is relatively unlikely.

Score: 5/10 — Correct formula setup but the numerical computation of the large combinations is wrong, giving 10.86% which doesn’t make sense. Then notes the binomial gives 3.17% but doesn’t realize the huge discrepancy means the hypergeometric calculation was botched. Fails to self-correct.

Test 4 Winner: DeepSeek 🏆

Test 5: Data Analysis & Explanation

Prompt: “Explain what a p-value is to someone who has never taken a statistics class. Then explain why p < 0.05 has been criticized. Use a real-world analogy.”

DeepSeek’s Output

What’s a p-value? (The Coin Flip Analogy)

Imagine you suspect your friend’s coin is rigged. You start flipping it and get 8 heads out of 10 flips. Is the coin unfair, or did you just get lucky?

The p-value answers this question: “If the coin were perfectly fair, how likely would it be to get a result this extreme (or more extreme) just by chance?”

For 8 heads out of 10 with a fair coin, the probability is about 5.5%. That’s your p-value: 0.055.

A small p-value means: “This would be really unlikely if nothing special were happening — so maybe something IS going on.” A large p-value means: “Eh, this could easily happen by chance.”

Why p < 0.05 has been criticized:

The rule “call it significant if p < 0.05” was popularized in the 1920s by statistician Ronald Fisher as a convenient threshold. It stuck. But it’s caused real problems:

1. It’s arbitrary. There’s nothing magical about 5%. A p-value of 0.049 gets published and celebrated. A p-value of 0.051 gets filed in a drawer. The actual difference in evidence between these two results is negligible.

2. P-hacking. Researchers can run dozens of slightly different analyses until one happens to give p < 0.05. This is like flipping coins until one of them “looks” unfair by chance. A 2015 study estimated that most published psychology findings using p < 0.05 failed to replicate.

3. It doesn’t tell you what you actually want to know. A p-value does NOT tell you the probability that your hypothesis is true. It tells you how surprising your data would be IF your hypothesis were false. These are very different things. (Imagine a court saying “there’s a 4% chance the evidence would look like this if the defendant were innocent” — that’s not the same as “96% chance they’re guilty.”)

4. Sample size distortion. With a large enough sample, trivially small effects become “statistically significant.” A drug that lowers blood pressure by 0.1 mmHg (clinically meaningless) will show p < 0.05 if you test 100,000 people.

Many statisticians now advocate for reporting effect sizes, confidence intervals, and using Bayesian methods alongside or instead of p-values.

Score: 10/10 — Brilliant explanation. The coin analogy is perfect for a beginner. Covers all four major criticisms with clear examples. The courtroom analogy for point 3 is especially effective. The Fisher history adds credibility.

Gemini’s Output

Understanding P-Values: The Fire Alarm Analogy

Imagine you have a fire alarm in your kitchen. Sometimes it goes off because there’s an actual fire. Sometimes it goes off because you burned toast. The alarm can’t tell you which one — it just detects smoke.

A p-value works similarly. When scientists do an experiment and see a result, they ask: “Could this have happened just by random chance, even if our theory is wrong?”

The p-value is the probability of seeing your result (or something more extreme) if there really is nothing going on — if it’s just “burnt toast” and not a “real fire.”

• A small p-value (like 0.01) means: “It would be very unlikely to see this result by chance alone. This looks like a real fire.”
• A large p-value (like 0.45) means: “This could easily happen by chance. Probably just burnt toast.”

Why the “p < 0.05” rule is controversial:

Scientists have traditionally used 0.05 (5%) as a cutoff: if the p-value is below 0.05, they declare the result “statistically significant.” But this practice has drawn serious criticism:

1. The threshold is arbitrary. Why 5% and not 3% or 10%? There’s no deep scientific reason. It was a convention suggested by Ronald Fisher as a rough guide, and it became a rigid rule.

2. It creates black-and-white thinking. A study with p = 0.049 gets published as a “significant finding.” A study with p = 0.051 might not get published at all. But the actual difference in evidence is tiny.

3. It encourages “p-hacking.” Researchers might (consciously or unconsciously) tweak their methods — analyzing data in different ways, adding more participants, or testing many variables — until they find p < 0.05. It’s like adjusting a fire alarm to be so sensitive it goes off from a candle.

4. Significance ≠ Importance. A statistically significant result might be practically meaningless. If you test a million people, you can find a “significant” difference in almost anything, even if the actual effect is tiny.

Many journals and organizations, including the American Statistical Association, now urge scientists to go beyond p-values and also report effect sizes and confidence intervals.

Score: 9/10 — The fire alarm analogy is creative and mostly works, though the coin analogy (DeepSeek’s) maps more precisely to what a p-value actually does. Good coverage of the criticisms. Slightly less precise on point 3 — misses the key distinction between P(data|hypothesis) and P(hypothesis|data). Still excellent for a lay audience.

Test 5 Winner: DeepSeek 🏆

Final Scores

DeepSeek vs Gemini: The Verdict

DeepSeek wins 4 out of 5 tests, with one tie in creative writing.

This result is remarkable considering DeepSeek is entirely free and open-source while Gemini is backed by Google’s massive infrastructure and resources.

When to Use DeepSeek

• Coding tasks — DeepSeek’s code output was cleaner, better documented, and more thorough
• Math and reasoning — stronger at showing work and self-correcting
• Budget-conscious users — completely free with no usage limits on the chat interface
• Privacy-focused users — can be self-hosted since it’s open-source
• Technical writing — produces more detailed, structured explanations

When to Use Gemini

• Google ecosystem integration — if you live in Google Workspace, Gemini connects directly to Gmail, Docs, Drive
• Real-time information — Gemini has live Google Search integration for current events
• Multimodal tasks — image analysis, video understanding (not tested here but a clear Gemini strength)
• Visual/creative projects — Gemini’s image generation capabilities (via Imagen)
• Enterprise use — Google’s compliance and data governance infrastructure

The Bigger Picture

DeepSeek’s performance here is a testament to what open-source AI can achieve. The quality gap between free, open models and proprietary offerings is closing fast. For text-based tasks, DeepSeek is genuinely competitive with — and sometimes better than — Google’s best.

That said, Gemini’s strength isn’t just text generation. Its integration with Google’s ecosystem, multimodal capabilities, and enterprise features make it a fundamentally different product. If you’re choosing between them, the question isn’t really “which is smarter?” but “which fits your workflow?”

Pricing Comparison

The Bottom Line

For pure text AI capability, DeepSeek delivers extraordinary value. For users embedded in Google’s ecosystem who need multimodal features and enterprise integration, Gemini justifies its price. Many power users will find both tools complement each other well.

Last updated: February 2026. We re-test every major model update. Subscribe to our newsletter for the latest comparison data.

Considering other options? Check out our comparisons: ChatGPT vs DeepSeek, Claude vs DeepSeek, ChatGPT vs Gemini, Claude vs Gemini.